Despite the great development of document summarization techniques nowadays,
factual inconsistencies between the generated summaries and the original text
still occur from time to time. This paper proposes a prefix-tuning-based
approach that uses a set of trainable continuous prefix prompt together with
discrete prompts to aid model generation, which makes a significant impact on
both CNN/Daily Mail and XSum summaries generated using GPT-2. The improvements
on fact preservation in the generated summaries indicates the effectiveness of
adopting this prefix-tuning-based method in knowledge-enhanced document
summarization, and also shows a great potential on other natural language
processing tasks.

本文提出了基于前缀微调（prefix tuning）的方法，使用一组可训练的连续前缀提示和离散提示来辅助模型生成，显著提高了使用 GPT-2 生成的 CNN/Daily Mail 和 XSum 摘要的事实保留。此方法在知识增强的文档摘要中表现出了其有效性，并显示了在其他自然语言处理任务中的巨大潜力。